Alternative Methods for Modeling Fatigue and Performance

Aviation Space and Environmental Medicine

Volumn 75, Issue 3, 2004, Pages

  Reifman, Jaques ^a  

^a U S ARMY MEDICAL RESEARCH AND MATERIEL COMMAND   (United States)

Author keywords

Artificial neural networks; Cognitive models; Fatigue and performance models; Gray models; Hybrid models

Indexed keywords

ARMY; ARTIFICIAL NEURAL NETWORK; COGNITION; CONFERENCE PAPER; FATIGUE; MODEL; PERFORMANCE; QUANTITATIVE ANALYSIS; RELIABILITY; SLEEP; TECHNIQUE;

FATIGUE; HUMANS; KNOWLEDGE; MILITARY MEDICINE; MODELS, BIOLOGICAL; NEURAL NETWORKS (COMPUTER); REPRODUCIBILITY OF RESULTS; SLEEP; STATISTICS, NONPARAMETRIC; TASK PERFORMANCE AND ANALYSIS;

EID: 1442304795     PISSN: 00956562     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (33)

1. Acherman P, Borbély AA. Simulation of daytime vigilance by the additive interaction of a homeostatic and a circadian process. Biol Cybern 1994; 71:115-21.
2. Belenky G, Balkin TJ, Redmond DP, et al. Sustaining performance during continuous operations: the U.S. Army's sleep management system. In: Friedl K, Lieberman H, Ryan DH, Bray GA, eds. Countermeasures for battlefield stressors: Pennington center nutrition series. Baton Rouge, LA: Louisiana State University Press; 1999:197-205.
3. Borbély AA. A two process model of sleep regulation. Hum Neurobiol 1982; 1:195-204.
4. Dinges DF, Achermann P. Future considerations for models of human neurobehavioral function [Commentary]. J Biol Rhythms 1999; 14:598-601.
5. Dinges DF. Critical research issues in development of biomathematical models of fatigue and performance. Aviat Space Environ Med 2004; 75 (3, Suppl.):A181-91.
6. Efrom B, Tibshirani RJ. An introduction to the bootstrap. New York: Chapman & Hall; 1993.
7. Fatigue and performance modeling workshop. 2002; Retrieved 10 June 2003 from http://fatigue.anteon.com/.
8. Folkard S, Åkerstedt T, Macdonald I, et al. Beyond the three-process model of alertness: Estimating phase, time on shift and successive night effects. J Biol Rhythms 1999; 14:577-87.
9. Hassberger JA, Lee JC. A simulation-based expert system for nuclear power plant diagnostics. Nucl Sci Eng 1989; 102:153-71.
10. Haykin S. Neural networks: A comprehensive foundation. 2nd ed. Englewood Cliffs, NJ: Prentice Hall; 1999.
11. Heskes T. Practical confidence and prediction intervals. In: Mozer MC, Jordan MI, Petsche T, eds. Advances in neural network information processing systems 9. Cambridge, MA: The MIT Press; 1997; 176-82.
12. Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators. Neural Netw 1989; 2:359-66.
13. Hoyt RW, Reifman J, Coster TS, Buller MJ. Combat medical informatics: Present and future. Proceedings of the AMIA 2002 Annual Symposium; 2002 Nov 9-13; San Antonio, TX. Bethesda, MD: American Medical Informatics Association; 2002: 335-9.
14. Johnson MJ, Belenky G, Redmond DP, et al. Modulating the homeostatic process to predict performance during chronic sleep restriction. Aviat Space Environ Med 2004; 75 (3, Suppl.): A141-6.
15. Kalman RE, Bucey KS. New results in linear filtering and predictive theory. Transactions of the ASME. J Basic Eng 1965; 83D: 95-108.
16. Kramer MA. Autoassociative neural networks. Comput Chem Eng 1992; 16:313-28.
17. Krogh A, Palmer RG, Hertz JA. Introduction to the theory of neural computation. New York: Perseus Books Group; 1991.
18. Makeig S, Neri DF. A proposal for integrated shipboard alertness management. In: Friedl K, Lieberman H, Ryan DH, Bray GA, eds. Countermeasures for battlefield stressors: Pennington center nutrition series. Baton Rouge: Louisiana State University Press; 1999:1-8.
19. Mallis MM, Mejdal S, Nguyen TT, Dinges DF. Summary of the key features of seven biomathematical models of human fatigue and performance. Aviat Space Environ Med 2004; 75 (3, Suppl.):A4-14.
20. Moore-Ede M, Heitmann A, Croke D, et al. Circadian alertness simulator for fatigue risk assessment in transportation: Application to reduce frequency and severity of truck accidents. Aviat Space Environ Med 2004; 75 (3, Suppl.):A107-18.
21. Psichogios DC, Ungar LH. A hybrid neural network: First principles approach to process modeling. Am Inst Chem Eng J 1992; 38:1499-511.
22. Reifman J, Vitela JE. Accelerating learning of neural networks with conjugate gradients for nuclear power plant applications. Nucl Technology 1994; 106:225-41.
23. Reifman J, Feldman EE, Wei TYC, Glickert RW. An intelligent emissions controller for fuel lean gas rebum in coal-fired power plants. J Air Waste Manag Assoc 2000; 50:240-51.
24. Reifman J, Feldman EE. Multilayer perceptron for nonlinear programming. Comput Ops Res 2002; 29:1237-50.
25. Reifman J, Gander P. Commentary on the three process model of alertness and broader modeling issues. Aviat Space Environ Med 2004; 75 (3, Suppl.):A84-8.
26. Rumelhart DE, Hinton GE, Williams RJ. Learning internal representation by error propagation. In: Rumelhart DE, McClelland JC, eds. Parallel distributed processing: Explorations in the microstructure of cognition. Cambridge, MA: The MIT Press; 1986:318-62.
27. Stubberud SC, Lobbia RN, Owen M. Adaptive state estimation using artificial neural networks. In: Dagli CH, ed. Intelligent engineering systems through artificial neural networks. New York: ASME Press; 1995:5:531-6.
28. Thompson ML, Kramer MA. Modeling chemical processes using prior knowledge and neural networks. Am Inst Chem Eng J 1994; 40:1328-40.
29. Tibshirani R. A comparison of some error estimates for neural network models. Neural Comput 1996; 8:152-63.
30. van Can HJL, Hellinga C, Luyben KCAM, et al. Strategy for dynamic process modeling based on neural networks in macroscopic balances. Am Inst Chem Eng J 1996; 42:3403-18.
31. Van Dongen HPA. Comparison of mathematical model predictions to experimental data of fatigue and performance. Aviat Space Environ Med 2004; 75 (3, Suppl.):A15-36.
32. Vilim RB, Garcia HE, Chen FW. An identification scheme combining first principle knowledge, neural networks, and the likelihood function. IEEE Trans Control Sys Tech 2001; 9:186-99.
33. Wilson JA, Zorzetto FM. A generalized approach to process state estimation using hybrid artificial neural networks/mechanistic models. Comput Chem Eng 1997; 21:951-63.